feat: add configuration for open ai

[dciborow]

based on the auto-gpt configuration context.

################################################################################
### LLM PROVIDER
################################################################################

### OPENAI
## OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)

## NOTE: https://platform.openai.com/docs/api-reference/completions
# The temperature setting in language models like GPT controls the balance between predictable and random responses. 
# Lower temperature makes the responses more focused and deterministic, while higher temperature makes them more 
# creative and varied. The temperature range typically goes from 0 to 2 in OpenAI's implementation.
##
## TEMPERATURE - Sets temperature in OpenAI (Default: 0)
##
###

## USE_AZURE - Use Azure OpenAI or not (Default: False)
OPENAI_API_KEY=your-openai-api-key
# TEMPERATURE=0
# USE_AZURE=False

### AZURE
# moved to `azure.yaml.template`

################################################################################
### LLM MODELS
################################################################################

## SMART_LLM_MODEL - Smart language model (Default: gpt-4)
## FAST_LLM_MODEL - Fast language model (Default: gpt-3.5-turbo)
# SMART_LLM_MODEL=gpt-4
# FAST_LLM_MODEL=gpt-3.5-turbo

### LLM MODEL SETTINGS
## FAST_TOKEN_LIMIT - Fast token limit for OpenAI (Default: 4000)
## SMART_TOKEN_LIMIT - Smart token limit for OpenAI (Default: 8000)
## When using --gpt3only this needs to be set to 4000.
# FAST_TOKEN_LIMIT=4000
# SMART_TOKEN_LIMIT=8000

### EMBEDDINGS
## EMBEDDING_MODEL       - Model to use for creating embeddings
## EMBEDDING_TOKENIZER   - Tokenizer to use for chunking large inputs
## EMBEDDING_TOKEN_LIMIT - Chunk size limit for large inputs
# EMBEDDING_MODEL=text-embedding-ada-002
# EMBEDDING_TOKENIZER=cl100k_base
# EMBEDDING_TOKEN_LIMIT=8191

[kunjshukla]

hey, I would like to contribute to this issue. Please assign this to me.